Correlation between the Size of the Solid Component on Thin-Section CT and the Invasive Component on Pathology in Small Lung Adenocarcinomas Manifesting as Ground-Glass Nodules

AbstractControversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

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Arc Concave Sign on Thin-section Computed Tomography：A Novel Predictor for Invasive Pulmonary Adenocarcinoma in pure Ground-glass Nodules

European Journal of Radiology ◽

10.1016/j.ejrad.2021.109683 ◽

2021 ◽

pp. 109683

Author(s):

Gangze Fu ◽

M.D Huibo Yu ◽

Jinjin Liu ◽

Tianyi Xia ◽

Lanting Xiang ◽

...

Keyword(s):

Thin Section ◽

Pulmonary Adenocarcinoma ◽

Ground Glass ◽

Ground Glass Nodules

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Should resection extent be decided by total lesion size or solid component size in ground glass opacity-containing lung adenocarcinomas?

Translational Lung Cancer Research ◽

10.21037/tlcr-21-132 ◽

2021 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Boyu Lin ◽

Rui Wang ◽

Liang Chen ◽

Zhitao Gu ◽

Chunyu Ji ◽

...

Keyword(s):

Ground Glass Opacity ◽

Lesion Size ◽

Solid Component ◽

Ground Glass ◽

Component Size ◽

Lung Adenocarcinomas

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Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules

Frontiers in Oncology ◽

10.3389/fonc.2021.698053 ◽

2021 ◽

Vol 11 ◽

Author(s):

Bin Wang ◽

Preeti Hamal ◽

Xue Meng ◽

Ke Sun ◽

Yang Yang ◽

...

Keyword(s):

Prediction Model ◽

Clinical Features ◽

Discrimination Performance ◽

Ground Glass ◽

Atypical Adenomatous Hyperplasia ◽

Adenomatous Hyperplasia ◽

Model Based ◽

Validation Set ◽

Lung Adenocarcinomas ◽

Ground Glass Nodules

ObjectivesWe aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH.MethodsSurgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set.ResultsFour hundred and eighty-five eligible cases were included and randomly assigned to the training (n = 339) or the validation sets (n = 146). The developed radiomics prediction model showed good discrimination performance to distinguish AAH from adenocarcinomas in both the training and the validation sets, with, respectively, 84.1% and 82.2% of accuracy, and AUCs of 0.899 (95% CI: 0.867–0.931) and 0.881 (95% CI: 0.827–0.936).ConclusionThe prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

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